動態運補及私人交換之公共自行車系統 ─以台北市微笑單車系統為例

Abstract

降低公共自行車系統使用率的主因來自有限的自行車以及車柱數量。此研究中，我們考慮運補車和私人交換。對於自行車再配置，運補車可以將供需不平衡的自行車做重新的分配。對於私人交換，當車柱滿時，我們讓顧客可以毋須透過車柱還車，打破原有的人對車柱，改成可以人對人的交換，即是透過如APP來進行交換。本研究中考慮三個核心目標，首先是最大化自行車旅程次，此目標與取代私人交通工具是高度相關的。第二是最大化系統的利潤，系統內包含自行車旅程收入、運補車運作、車柱成本和APP系統的成本。最後是決定自行車和運補車之間該如何運行，包含自行車和車柱的初始配置量。我們使用台北市微笑單車於2013年的真實資料做為我們的參數。並使用兩種方法:線性放鬆以及粒子群演算法來驗證我們的數學模型。

Subject to limited numbers of bicycles and docks, unbalanced distribution of bicycles is an important issue to decrease the system utilization. In this study, we consider trucks for bicycle reallocation and APPs for private exchange. For bicycle reallocation, trucks are hired to dynamically redistribute bicycles among unbalanced stations. For private exchange, APPs can transfer the registration of bicycles face-to-face without docks. It allows bicycle exchange even when all docks are full. Three core objectives are considered in this study. The first is to maximize the total trips of bicycles, which is highly related to the private vehicle replacement ratio. The second is to maximize the net profit of system, which is highly related to the sustainability of bicycle-sharing systems. The net profit includes the income of bicycle trips and the costs of truck operation, dock construction, and APP system maintenance. The third is to optimize the fleet sizes of bicycles and trucks and to decide the best initial distribution of bicycles and docks. Integer programming models are built and real data of Taipei YouBike system in 2013 are adopted for numerical study. Two methods, linear relaxation and Particle Swarm Optimization are adopted to solve and verify our models.